IT Data Analytics Specialist

1. Contribution to IT Strategy by facilitating exploration through POC/POV - under the supervision and guidance of her/his primary Community of Practice Lead and Product Group Manager based in Switzerland
• Articulates a vision and roadmap for ingestion, cleansing, staging, harmonization and exploitation of data as a valued corporate asset in relation with existing functional priorities to help Product Managers explore new ways to solve complex business problems
• Works on data requirements (provided by UX and Data Scientists) that will be used to train and develop models and algorithms to solve business challenges
• As part of a POC/POV, creates data ingestion strategies, prepares data, helps in variable creation, creates information models or data staging strategies and provides necessary cleansing activities
• Manages data lifecycle during the POC/POV and also starts creating strategies to embed them into an industrialized model or service operations
• Manages data in secure and compliant way even during the POC/POV to avoid any potential risk
• Works with Lead markets, function, GMB/RMB to conduct the POC/POV and brings it to a closure
2. Operational effectiveness and efficiency by helping Industrialize proven models
• Helps product teams and Solution Architects industrialize information models proven during the POC/POV by devising data collection procedures to include information that is relevant for building analytic systems
• Helps Solution Architects develop processes and tools to monitor and information model performance and data accuracy ongoing
• Helps technical specialists design better descriptive and prescriptive analytics solutions by providing the basis of semantic models that can be used to visualize information and develop reports on results of data analysis to help facilitate new KPI/PPI discussions
• Helps technical specialists in api/interfacing technologies to understand better how to acquire data and create the ingestion layers of industrialized information models
• Educates and influences use of services rather than full automation where manual intervention makes sense based on the cost benefit analysis
3. Stakeholder engagement
• Influences information architects on what should be part of core data asset in the company and what has repeat value.
• Socializes best practices to analytics and product teams as well as facilitates market enablement of similar tasks
• Works with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions and provide data sourcing advisory
• Influences product teams including Solution Architects through presentation of data-based recommendations into evolving operational solutions with new and enhanced models including creating effective semantic models and effective interfacing and api connectors
• Champions best practices for data management across delivery as well as recipient organizations